The design of most leading edge processors and ASICs rely on steps of optimization, with the three key optimization points being Performance, Power, and Area (and sometimes Cost). Once the architecture of a chip is planned, it comes down to designing the silicon of that chip for a given process node technology, however there are many different ways to lay the design out. Normally this can take a team of engineers several months, even with algorithmic tools and simulation to get a good result, however that role is gradually being taken over with Machine Learning methods. Cadence today is announcing its new Cerebrus integrated ML design tool to assist with PPA optimization – production level silicon is already being made with key partners as the tool directly integrates into Cadence workflows.
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